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A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms

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Abstract

Movement execution results in the simultaneous generation of movement-related potentials (MRP) as well as changes in the power of Mu and Beta rhythms. This paper proposes a new self-paced multi-channel BI that combines features extracted from MRPs and from changes in the power of Mu and Beta rhythms.

We developed a new algorithm to classify the high-dimensional feature space. It uses a two-stage multiple-classifier system (MCS). First, an MCS classifies each neurological phenomenon separately using the information extracted from specific EEG channels (EEG channels are selected by a genetic algorithm). In the second stage, another MCS combines the outputs of MCSs developed in the first stage.

Analysis of the data of four able-bodied subjects shows the superior performance of the proposed algorithm compared with a scheme where the features were all combined in a single feature vector and then classified.

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Notes

  1. Although the evidence from the literature suggests the Beta ERS occurs in the first second after the movement (which was confirmed in this paper that by analyzing the averages of the of Beta rhythms over the epochs in the training set), t finish  = 2 seconds was chosen because the return of the power of the Mu rhythm to the baseline activity usually takes a much longer time. In this paper, we didn't study the optimal (or at least a sub-optimal) choice for t start and t finish . This research is left to future work.

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Acknowledgments

This work was supported in part by the NSERC under Grant 90278-06 and the CIHR under Grant MOP-72711. This research was enabled by the use of WestGrid computing resources, which are funded in part by the Canada Foundation for Innovation, Alberta Innovation and Science, BC Advanced Education, and the participating research institutions. WestGrid equipment is provided by IBM, Hewlett Packard and SGI. The authors also would like to thank Mr. Craig Wilson for his valuable comments on this paper.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of British Columbia, 2356 Main Mall, Vancouver, BC, Canada, V6T 1Z4

    Mehrdad Fatourechi, Gary E. Birch & Rabab K. Ward

  2. Neil Squire Society, Burnaby, BC, Canada, V5M 3Z3

    Gary E. Birch

  3. Institute for Computing, Information and Cognitive Systems, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada, V6T 1Z4

    Gary E. Birch & Rabab K. Ward

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  1. Mehrdad Fatourechi
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Correspondence to Mehrdad Fatourechi.

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Fatourechi, M., Birch, G.E. & Ward, R.K. A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms. J Comput Neurosci 23, 21–37 (2007). https://doi.org/10.1007/s10827-006-0017-3

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